PurposeBesides the direct physical health consequences, through social isolation COVID-19 affects a considerably larger share of consumers with deleterious effects for their psychological well-being. Two vulnerable consumer groups are particularly affected: older adults and children. The purpose of the underlying paper is to take a transformative research perspective on how social robots can be deployed for advancing the well-being of these vulnerable consumers and to spur robotic transformative service research (RTSR).Design/methodology/approachThis paper follows a conceptual approach that integrates findings from various domains: service research, social robotics, social psychology and medicine.FindingsTwo key findings advanced in this paper are (1) a typology of robotic transformative service (i.e. entertainer, social enabler, mentor and friend) as a function of consumers' state of social isolation, well-being focus and robot capabilities and (2) a future research agenda for RTSR.Practical implicationsThis paper guides service consumers and providers and robot developers in identifying and developing the most appropriate social robot type for advancing the well-being of vulnerable consumers in social isolation.Originality/valueThis study is the first to integrate social robotics and transformative service research by developing a typology of social robots as a guiding framework for assessing the status quo of transformative robotic service on the basis of which it advances a future research agenda for RTSR. It further complements the underdeveloped body of service research with a focus on eudaimonic consumer well-being.

PurposeWith the advent of increasingly sophisticated AI, the nature of work in the service frontline is changing. The next frontier is to go beyond replacing routine tasks and augmenting service employees with AI. The purpose of this paper is to investigate whether service employees augmented with AI-based emotion recognition software are more effective in interpersonal emotion regulation (IER) and whether and how IER impacts their own affective well-being.Design/methodology/approachFor the underlying study, an AI-based emotion recognition software was developed in order to assist service employees in managing customer emotions. A field study based on 2,459 call center service interactions assessed the effectiveness of the AI in augmenting service employees for IER and the immediate downstream consequences for well-being relevant outcomes.FindingsAugmenting service employees with AI significantly improved their IER activities. Employees in the AI (vs control) condition were significantly more effective in regulating customer emotions. IER goal attainment, in turn, mediated the effect on employee affective well-being. Perceived stress related to exposure to the AI augmentation acted as a competing mediator.Practical implicationsService firms can benefit from state-of-the-art AI technology by focusing on its capacity to augment rather than merely replacing employees. Furthermore, signaling IER goal attainment with the help of technology may provide uplifting consequences for service employee affective well-being.Originality/valueThe present study is among the first to empirically test the introduction of an AI-fueled technology to augment service employees in handling customer emotions. This paper further complements the literature by investigating IER in a real-life setting and by uncovering goal attainment as a new mechanism underlying the effect of IER on the well-being of the sender.

Service interactions run a gamut from an instrumental self-focus to full social appreciation. Observing another customer's incivility toward a frontline employee can emphasize social concerns as guiding principles for the observer's own service interaction. Five studies test these dynamics; the results reveal that an incivility incident leads observers to prioritize social over market concerns. This reprioritization becomes manifest in a subsequent service interaction through increased feelings of warmth toward the employee who experienced incivility. In turn, feelings of warmth prompt observers to provide emotional support to the affected employee. Yet such prosocial inclinations are less likely when an employee is held responsible for or reciprocates incivility. Finally, this article also examines the effects of different employee reaction strategies on observers' inferences about the employee and the service firm, showing that observers are most positively disposed toward the employee and the firm when the former reacts to incivility with a polite reprimand. Together, the results suggest that, contrary to past theorizing, observing customers may contribute to employee well-being, contingent on appropriate employee responses. Notably, the commonly prescribed polite, submissive employee reaction that requires emotional labor may not be the most desirable reaction-neither for the employee nor for the firm.

Why do people try to influence the way others feel? Previous research offers two competing accounts of people’s motives for attempting to regulate others’ emotions. The instrumental account holds that people use interpersonal emotion regulation to benefit their own goal pursuit. Conversely, the prosocial account holds that people use interpersonal emotion regulation to benefit others’ goals. This article juxtaposes these accounts across two studies. Study 1 demonstrates that when given the chance to benefit themselves through their interpersonal emotion regulation, people choose to do so, even when this involves making a friend feel unpleasant. Yet when given the chance to benefit a friend through interpersonal emotion regulation, with no personal gains, people also choose to do so. Study 2 reveals no overall tendencies toward either motive when people can choose between benefitting themselves or a friend through their interpersonal emotion regulation. However, people’s motives can be reliably predicted by their values: individuals with high values of care and concern for others show a greater tendency to regulate a friend’s emotions prosocially and a lower tendency toward instrumentality.

Consumers are frequently bombarded with a myriad of marketing tactics. One tactic regularly employed by thrift-oriented brands is to highlight low prices, discounts, and sales promotions. When consumers encounter these low-price signals, they may adopt a price conscious mentality, that is, a singular focus on getting the cheapest deal. A price conscious mentality is likely beneficial for consumers, as it helps them save money. However, it is also possible that it has negative implications, particularly for how consumers perceive and interact with other human beings in the marketplace, such as customer service employees. The current research addresses this issue by investigating how consumers' price conscious mentality impacts their perceptions of employees' humanity. Results from four studies demonstrate that a price conscious mentality can lead consumers away from fully recognizing the human qualities of employees. The findings also suggest that this subtle form of dehumanization can result in harsher treatment of employees when they provide less than satisfactory service.

PurposeA vast body of literature has documented the negative consequences of stress on employee performance and well-being. These deleterious effects are particularly pronounced for service agents who need to constantly endure and manage customer emotions. The purpose of this paper is to introduce and describe a deep learning model to predict in real-time service agent stress from emotion patterns in voice-to-voice service interactions.Design/methodology/approachA deep learning model was developed to identify emotion patterns in call center interactions based on 363 recorded service interactions, subdivided in 27,889 manually expert-labeled three-second audio snippets. In a second step, the deep learning model was deployed in a call center for a period of one month to be further trained by the data collected from 40 service agents in another 4,672 service interactions.FindingsThe deep learning emotion classifier reached a balanced accuracy of 68% in predicting discrete emotions in service interactions. Integrating this model in a binary classification model, it was able to predict service agent stress with a balanced accuracy of 80%.Practical implicationsService managers can benefit from employing the deep learning model to continuously and unobtrusively monitor the stress level of their service agents with numerous practical applications, including real-time early warning systems for service agents, customized training and automatically linking stress to customer-related outcomes.Originality/valueThe present study is the first to document an artificial intelligence (AI)-based model that is able to identify emotions in natural (i.e. nonstaged) interactions. It is further a pioneer in developing a smart emotion-based stress measure for service agents. Finally, the study contributes to the literature on the role of emotions in service interactions and employee stress.

This initiative examined systematically the extent to which a large set of archival research findings generalizes across contexts. We repeated the key analyses for 29 original strategic management effects in the same context (direct reproduction) as well as in 52 novel time periods and geographies; 45% of the reproductions returned results matching the original reports together with 55% of tests in different spans of years and 40% of tests in novel geographies. Some original findings were associated with multiple new tests. Reproducibility was the best predictor of generalizability—for the findings that proved directly reproducible, 84% emerged in other available time periods and 57% emerged in other geographies. Overall, only limited empirical evidence emerged for context sensitivity. In a forecasting survey, independent scientists were able to anticipate which effects would find support in tests in new samples.

Social robots are increasingly being deployed in a wide variety of consumer-facing services, where they co-create value with and for the benefit of the consumers they interact with (Lu et al., 2020;Wirtz et al., 2018). Robots welcome customers to restaurants and hotels, entertain children, read cooking recipes at home, give additional information about products in stores, or assist the elderly with walking to support their health (Henschel et al., 2021;KPMG, 2016). What all these robots delivering services to consumers have in common is that they represent an "information technology in a physical embodiment, providing customized services by performing physical as well as nonphysical tasks with a high degree of autonomy" (Jörling et al., 2019, p. 405). This integration of robots into the marketplace reshapes service interactions and also challenges some fundamental principles of consumer-firm interactions (Kaartemo & Helkkula, 2018;Subramony et al., 2018). While service robots come with different levels of intelligence (Huang & Rust, 2018) and in various manifestations (Wirtz et al., 2018), embodied robots engaging in social interactions with consumers are expected to ignite what could be the most dramatic transformation of the consumer service landscape in the age of ser-